The curse of dimensionality in data mining and time series prediction. In International Work-Conference on Artificial Neural Networks; Springer: Berlin/Heidelberg, Germany, 2005; pp. 758-770.M. Verleysen, D. Francois, "The curse of dimensionality in data mining and time series prediction", I...
Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as $$k$$ -neares
A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction i...
It is so easy and convenient to collect data An experiment Data is not collected only for data mining Data accumulates in an unprecedented speed Data preprocessing is an important part for effective machine learning and data mining Dimensionality reduction is an effective approach to downsizing data ...
With rapid growth in ecommerce applications or any e-business application use of high dimensional data becomes very common. Thus mining this high dimensional data is the major problem faced by any business application. Time series data is also high dimensional data where information is collected wit...
But mining in high dimensional data is extraordinarily difficult because of the curse ofdimensionality. 由于这种数据存在的普遍性,使得对高维数据挖掘的研究有着非常重要的意义. 互联网 These regenerative and beneficial relationships give a system complexitydimensionality, and thus, resiliency. ...
What is the need for Dimensionality Reduction in Data Mining? Data mining is the process of observing hidden patterns, relations, and anomalies within vast datasets in order to estimate outcomes. Vast datasets have many variables increasing at an exponential rate. Therefore, finding and analyzing pat...
Dimensionality reduction for density ratio estimation in high-dimensional spaces. The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it c... M Sugiyama,M Kawanabe,PL Chui - 《Neural Networks》 被引...
It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time. 展开 关键词: Keywords: Data mining Dimensionality reduction Indexing and retrieval Time series ...
However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of ...